Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	140,840
2	Rhode Island	139,609
3	South Dakota	138,507
4	Utah	123,847
5	Tennessee	121,816
6	Arizona	118,379
7	Iowa	115,520
8	Nebraska	113,501
9	Wisconsin	113,322
10	Oklahoma	113,200
11	South Carolina	112,173
12	New Jersey	112,063
13	Arkansas	111,183
14	Alabama	107,585
15	Indiana	107,283
16	Delaware	106,841
17	Kansas	106,631
18	Illinois	105,395
19	New York	104,995
20	Idaho	104,964
21	Mississippi	104,717
22	Florida	103,744
23	Nevada	102,244
24	Minnesota	101,785
25	Montana	101,773
26	Georgia	101,191
27	Wyoming	100,333
28	Kentucky	100,001
29	Massachusetts	99,766
30	Texas	99,688
31	Louisiana	98,497
32	Missouri	97,781
33	Connecticut	94,928
34	California	94,644
35	New Mexico	94,164
36	Michigan	93,320
37	North Carolina	92,921
38	Alaska	92,213
39	Ohio	91,604
40	Pennsylvania	89,969
41	Colorado	88,893
42	West Virginia	85,223
43	Virginia	77,129
44	Maryland	73,878
45	New Hampshire	69,629
46	District of Columbia	67,465
47	Washington	52,999
48	Puerto Rico	51,440
49	Maine	45,338
50	Oregon	43,584
51	Vermont	36,577
52	Hawaii	23,010

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	454
2	Colorado	369
3	Rhode Island	294
4	Pennsylvania	275
5	Minnesota	266
6	Florida	250
7	New Jersey	243
8	Maine	233
9	Illinois	229
10	Alaska	223
11	North Dakota	213
12	Delaware	198
13	West Virginia	197
14	Massachusetts	192
15	Oregon	191
16	Washington	186
17	New Hampshire	181
18	North Carolina	180
19	New York	174
20	Indiana	172
21	Montana	170
22	South Dakota	170
23	Kentucky	166
24	Tennessee	164
25	Puerto Rico	153
26	Wisconsin	152
27	Maryland	145
28	Ohio	144
29	Wyoming	144
30	Connecticut	141
31	Iowa	139
32	Nevada	135
33	Texas	135
34	Utah	135
35	Virginia	133
36	Idaho	131
37	South Carolina	131
38	Georgia	130
39	Nebraska	125
40	Missouri	119
41	District of Columbia	111
42	Vermont	110
43	Louisiana	104
44	Arizona	99
45	New Mexico	95
46	Arkansas	83
47	Alabama	79
48	Mississippi	74
49	Kansas	72
50	Oklahoma	55
51	Hawaii	54
52	California	44

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,874
2	New York	2,656
3	Massachusetts	2,552
4	Rhode Island	2,519
5	Mississippi	2,417
6	Arizona	2,377
7	Connecticut	2,267
8	Louisiana	2,231
9	Alabama	2,220
10	South Dakota	2,217
11	Pennsylvania	2,049
12	North Dakota	1,997
13	Indiana	1,979
14	New Mexico	1,936
15	Illinois	1,913
16	Arkansas	1,900
17	Iowa	1,879
18	Michigan	1,872
19	South Carolina	1,844
20	Georgia	1,839
21	Nevada	1,772
22	Tennessee	1,770
23	Texas	1,736
24	Kansas	1,723
25	Oklahoma	1,715
26	Delaware	1,667
27	Ohio	1,641
28	Florida	1,633
29	District of Columbia	1,565
30	California	1,560
31	Missouri	1,505
32	West Virginia	1,492
33	Kentucky	1,479
34	Montana	1,468
35	Maryland	1,442
36	Wisconsin	1,295
37	Minnesota	1,280
38	Virginia	1,259
39	Nebraska	1,222
40	Wyoming	1,221
41	North Carolina	1,206
42	Idaho	1,146
43	Colorado	1,115
44	New Hampshire	956
45	Washington	726
46	Puerto Rico	718
47	Utah	685
48	Oregon	594
49	Maine	581
50	Alaska	451
51	Vermont	394
52	Hawaii	339

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	8
2	Oklahoma	6
3	Georgia	4
4	New Jersey	4
5	New Mexico	4
6	Pennsylvania	4
7	South Carolina	4
8	Minnesota	3
9	New Hampshire	3
10	Puerto Rico	3
11	Wisconsin	3
12	Alabama	2
13	Delaware	2
14	District of Columbia	2
15	Florida	2
16	Idaho	2
17	Illinois	2
18	Indiana	2
19	Kentucky	2
20	Louisiana	2
21	Maine	2
22	Maryland	2
23	Missouri	2
24	Montana	2
25	Nevada	2
26	New York	2
27	North Carolina	2
28	North Dakota	2
29	Texas	2
30	Arizona	1
31	Arkansas	1
32	California	1
33	Colorado	1
34	Connecticut	1
35	Hawaii	1
36	Kansas	1
37	Massachusetts	1
38	Mississippi	1
39	Ohio	1
40	Rhode Island	1
41	Tennessee	1
42	Utah	1
43	Vermont	1
44	Virginia	1
45	Washington	1
46	Wyoming	1
47	Alaska	0
48	Iowa	0
49	Nebraska	0
50	Oregon	0
51	South Dakota	0
52	West Virginia	-27

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	361,511	1	99
Crowley	Colorado	357,862	2	99
Bent	Colorado	272,548	3	99
Dewey	South Dakota	248,133	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,355	186	94
Richland	South Carolina	110,302	996	68
York	South Carolina	108,538	1063	66
Orange	California	85,025	2171	30
Pierce	Washington	53,991	2875	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,558	1824	41
York	South Carolina	1,342	2073	34
Davidson	Tennessee	1,341	2075	33
Richland	South Carolina	1,311	2109	32
Pierce	Washington	714	2742	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.